Qiao Zhang

Sensorless Predictive Current Controlled DC–DC Converter With a Self-Correction Differential Current Observer

For a sensorless predictive current controlled boost dc-dc converter, its small-signal model that contains a number of parasitic parameters, is derived in this paper. This model indicates that the type of system becomes type 0 even with the correction of voltage loop proportional-integral controller, leading to the existence of output voltage steady-state error. Then a self-correction differential current observer (SDCO) is proposed to eliminate this steady-state error and gain high transient response speed. The self-correction part of the SDCO makes the system become type 1 to achieve no steady-state error for output voltage, whereas the differential part can guarantee that the intermediate calculation results do not overflow. By carrying out a series of simulation verifications, further investigation proves that the proposed algorithm has good robustness. Finally, the effectiveness of the proposed algorithm is verified by experimental results.

Sensorless Predictive Peak Current Control for Boost Converter Using Comprehensive Compensation Strategy

For a sensorless predictive-peak-current-controlled boost converter, the output voltage steady-state error cannot be eliminated by voltage loop PI controller. The basic cause for this is investigated through analysis and theoretical approaches. To eliminate the voltage steady-state error and achieve high-accuracy current estimation, a comprehensive compensation strategy is proposed. First, a compensation algorithm for output voltage sampling is introduced. It can not only effectively eliminate the output voltage steady-state error but also guarantee current observer convergence. The compensation schemes for component parasitic parameter effects and switching delay are also investigated. With this comprehensive compensation strategy, both the system transient response and current estimation accuracy are greatly improved. Finally, the effectiveness of the proposed algorithm is verified by experimental results.

The Hardware/Software Partitioning in Embedded System by Improved Particle Swarm Optimization Algorithm

Hardware/software partitioning is a key problem in hardware/software co-design. This paper presents a new hardware/software partitioning methodology based on improved particle swarm optimization algorithm. The model of the embedded system was constructed by directed acyclic graph to obtain the objective function. Then improvement strategies are introduced in order to overcome the problems of low precision and divergence in traditional particle swarm optimization algorithm. The improved algorithm can avoid local optimal solution efficiently and be conveniently implemented in the field of hardware/software partitioning.

Digital Sensorless Current Mode Control Based on Charge Balance Principle and Dual Current Error Compensation for DC–DC Converters in DCM

For discontinuous conduction mode (DCM) dc–dc converters with digital sensorless current mode (DSCM) control, an observed current error occurs, owing to a low-accuracy current observer. Moreover, a reference current error occurs due to a low-accuracy current controller or a finite dc gain of current loop. Conventionally, the observed current is compensated to increase current regulation accuracy, whereas the reference current error is neglected. However, for charge balance principle (CBP)-based DSCM (CBP-DSCM) control, this paper proves that the reference current should be compensated in a same quantity to that of observed current. Otherwise, single or unequal compensation leads to output voltage steady-state error. For this reason, a dual current error compensation strategy for CBP-DSCM control is proposed. It compensates the errors in a same quantity through a current error observer, which considers parasitics and calculates the current errors without approximation. To verify the proposed strategy, small-signal models with parasitics for both the converter and the controller are constructed by differential functions of key variables. Furthermore, converter stability is analyzed at typical operation condition, while the stability at various operation conditions is verified through robustness analysis. Finally, simulations and experimental results verify the analysis and the improved transient response of the converter.

A Sensorless Predictive Current Controlled Boost Converter by Using an EKF with Load Variation Effect Elimination Function

To realize accurate current control for a boost converter, a precise measurement of the inductor current is required to achieve high resolution current regulating. Current sensors are widely used to measure the inductor current. However, the current sensors and their processing circuits significantly contribute extra hardware cost, delay and noise to the system. They can also harm the system reliability. Therefore, current sensorless control techniques can bring cost effective and reliable solutions for various boost converter applications. According to the derived accurate model, which contains a number of parasitics, the boost converter is a nonlinear system. An Extended Kalman Filter (EKF) is proposed for inductor current estimation and output voltage filtering. With this approach, the system can have the same advantages as sensored current control mode. To implement EKF, the load value is necessary. However, the load may vary from time to time. This can lead to errors of current estimation and filtered output voltage. To solve this issue, a load variation elimination effect elimination (LVEE) module is added. In addition, a predictive average current controller is used to regulate the current. Compared with conventional voltage controlled system, the transient response is greatly improved since it only takes two switching cycles for the current to reach its reference. Finally, experimental results are presented to verify the stable operation and output tracking capability for large-signal transients of the proposed algorithm.

Multiloop Minimum Switching Cycle Control Based on Nonaveraged Current Discrete-Time Model for Buck Converter

Exploring high-performance controller for buck converter is challenging since it can be easily affected by converter model accuracy. In this paper, a novel nonaveraged current discrete-time (NCD) model is proposed, in which inductor current is expressed as time-varying equations during switch-on and switch-off states. It achieves higher accuracy than the conventional averaged model at high-frequency range, thus can be used to optimize high-speed controller design. Based on the NCD model, a multiloop minimum switching cycle (MMSC) control strategy, composed of output feedback (OF), line feed forward (LFF), and reference feed forward (RFF) loops, is proposed and tuned for buck converter operating in continuous conduction mode. Mutual influences among three loops are considered and eliminated by specifically designed LFF and RFF compensations, which adapt the OF compensation. With consideration of sampling and calculation delays, relationship between transient switching cycles and geometric center of controller poles is discovered from a calculated output voltage error series. Furthermore, theoretical minimum switching cycles are calculated by moving the center inside the unit cycle of complex plane, which ensures system stability. Moreover, load/line transient response and reference tracking time are simultaneously optimized to the minimum switching cycles. Effectiveness of the controller is proved by converter closed-loop pole/zero plots, transient response simulations, and experiments.

Bayesian estimation in dynamic framed slotted ALOHA algorithm for RFID system

This paper develops a novel dynamic framed slotted ALOHA (DFSA) algorithm based on Bayesian estimation to improve the throughput of the radio frequency identification (RFID) system. At first, four types of anti-collision algorithms for tag identification are analyzed. Then, the proposed DFSA based on Bayesian estimation is deduced and introduced. Compared with the conventional DFSA algorithms, this algorithm takes advantage of the evidence in previous frames as the a priori information of the current frame which can end up with more precise estimation of tag number and rational frame length adjustment. Finally, the common simulation tool from Matlab is used to demonstrate the effectiveness of the proposed new algorithm for the average throughput improvements of the RFID system.

An optimal current observer for predictive current controlled buck DC-DC converters.

In digital current mode controlled DC-DC converter, the conventional current sensor is either costly or has low accuracy. Thus, current observer, which could be realized based on digital circuit itself, is an ideal substitute for conventional current sensors. However, the observed current may diverge because of parasitic parameters. In this paper, an optimal current observer is proposed to solve the problem. Employing the optimal current observer based predictive current control, a buck converter, which shows preferable transient response than PID controlled converter is implemented. The effectiveness of the optimal current observer is experimentally demonstrated.

Hardware/Software Partitioning in Embedded System Based on Novel United Evolutionary Algorithm Scheme

Hardware/software partitioning is a key problem in hardware/software co-design and global optimums detection of the objective function is of vital importance in hardware/software partitioning. Though stochastic optimization strategies simulating evolution process are proved to be valuable tools, the balance between exploitation and exploration of which is difficult to be maintained. In this paper, the model of the embedded system was constructed by directed acyclic graph to obtain the objective function. Then some established techniques to improve the performance of evolutionary computation are discussed, such as uniform design,deflection and stretching the objective function, and space contraction. A novel scheme of evolutionary algorithms is proposed to solve the optimization problems through adding evolution operations to the searching space contracted regularly with these techniques. A typical evolutionary algorithm differential evolution is chosen to exhibit the performance of new scheme. The improved algorithm can avoid local optimal solution efficiently and be conveniently implemented in the field of hardware/software partitioning.